US7349746B2 - System and method for abnormal event detection in the operation of continuous industrial processes - Google Patents
System and method for abnormal event detection in the operation of continuous industrial processes Download PDFInfo
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- C—CHEMISTRY; METALLURGY
- C10—PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS; LUBRICANTS; PEAT
- C10G—CRACKING HYDROCARBON OILS; PRODUCTION OF LIQUID HYDROCARBON MIXTURES, e.g. BY DESTRUCTIVE HYDROGENATION, OLIGOMERISATION, POLYMERISATION; RECOVERY OF HYDROCARBON OILS FROM OIL-SHALE, OIL-SAND, OR GASES; REFINING MIXTURES MAINLY CONSISTING OF HYDROCARBONS; REFORMING OF NAPHTHA; MINERAL WAXES
- C10G11/00—Catalytic cracking, in the absence of hydrogen, of hydrocarbon oils
- C10G11/14—Catalytic cracking, in the absence of hydrogen, of hydrocarbon oils with preheated moving solid catalysts
- C10G11/18—Catalytic cracking, in the absence of hydrogen, of hydrocarbon oils with preheated moving solid catalysts according to the "fluidised-bed" technique
- C10G11/187—Controlling or regulating
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
Definitions
- This invention relates, in its broadest aspect, to preventing the escalation of process and equipment problems into serious incidents. It achieves this by first providing the operator with an early warning of a developing process or equipment problem, before the alarm system is activated, and by then providing the operator with key information for localizing and diagnosing the root cause of the problem.
- abnormal operations can have a significant economic impact (lost production, equipment damage), can cause environmental releases, and, in more severe cases, can endanger human life.
- An industrial consortium has estimated that abnormal events can cost between 3 and 8% of production capacity, which is over $10 billion for the US petrochemical industry.
- the current commercial practice is to notify the console operator of a process problem through process alarms.
- process alarms are defined by setting safe operating ranges on key process measurements (temperatures, pressures, flows, levels, and compositions). An alarm is given to the operator when the safe operating range of a measurement is violated.
- setting these alarm ranges is a delicate balance between giving the operator sufficient time to respond to the process problem and overwhelming him with a flood of alarms.
- the safe operating ranges of key process measurements are set wide to reduce low importance alarms. The negative result of these wide safe-operating ranges is that abnormal conditions can advance too far, and the operator is not left with enough time to take corrective actions to mitigate the abnormal event.
- a methodology and system has been developed to create and to deploy on-line, sets of models, which are used to detect abnormal operations and help the operator isolate the location of the root cause in an industrial process, in particular, a continuous process.
- the invention is applied to a chemical or refining process. More specifically, to a petrochemical process.
- the models employ principle component analysis (PCA).
- PCA principle component analysis
- These sets of models are composed of both simple models that represent known engineering relationships and principal component analysis (PCA) models that represent normal data patterns that exist within historical databases. The results from these many model calculations are combined into a small number of summary time trends that allow the process operator to easily monitor whether the process is entering an abnormal operation.
- FIG. 1 shows how the information in the online system flows through the various transformations, model calculations, fuzzy Petri nets and consolidations to arrive at a summary trend which indicates the normality/abnormality of the process areas.
- the heart of this system is the various models used to monitor the normality of the process operations.
- the PCA models described in this invention are intended to broadly monitor continuous refining and chemical processes and to rapidly detect developing equipment and process problems.
- the intent is to provide blanket monitoring of all the process equipment and process operations under the span of responsibility of a particular console operator post. This can involve many major refining or chemical process operating units (e.g. distillation towers, reactors, compressors, heat exchange trains, etc.) which have hundreds to thousands of process measurements.
- the monitoring is designed to detect problems which develop on a minutes to hours timescale, as opposed to long term performance degradation.
- the process and equipment problems do not need to be specified beforehand. This is in contrast to the use of PCA models cited in the literature which are structured to detect a specific important process problem and to cover a much smaller portion of the process operations.
- the method for PCA model development and deployment includes a number of novel extensions required for their application to continuous refining and chemical processes including:
- PCA models are supplemented by simple redundancy checks that are based on known engineering relationships that must be true during normal operations. These can be as simple as checking physically redundant measurements, or as complex as material and engineering balances.
- redundancy checks are simple 2 ⁇ 2 checks, e.g.
- Multidimensional checks are represented with “PCA like” models.
- FIG. 3 there are three independent and redundant measures, X 1 , X 2 , and X 3 . Whenever X 3 changes by one, X 1 changes by a 13 and X 2 changes by a 23 .
- This set of relationships is expressed as a PCA model with a single principle component direction, P.
- This type of model is presented to the operator in a manner similar to the broad PCA models.
- the gray area shows the area of normal operations.
- the principle component loadings of P are directly calculated from the engineering equations, not in the traditional manner of determining P from the direction of greatest variability.
- Each statistical index from the PCA models is fed into a fuzzy Petri net to convert the index into a zero to one scale, which continuously indicates the range from normal operation (value of zero) to abnormal operation (value of one).
- Each redundancy check is also converted to a continuous normal—abnormal indication using fuzzy nets.
- fuzzy nets There are two different indices used for these models to indicate abnormality; deviation from the model and deviation outside the operating range (shown on FIG. 3 ). These deviations are equivalent to the sum of the square of the error and the Hotelling T square indices for PCA models. For checks with dimension greater than two, it is possible to identify which input has a problem. In FIG. 3 , since the X 3 -X 2 relationship is still within the normal envelope, the problem is with input X 1 .
- Each deviation measure is converted by the fuzzy Petri net into a zero to one scale that will continuously indicate the range from normal operation (value of zero) to abnormal operation (value of one).
- the overall process for developing an abnormal event application is shown in FIG. 5 .
- the basic development strategy is iterative where the developer starts with a rough model, then successively improves that model's capability based on observing how well the model represents the actual process operations during both normal operations and abnormal operations.
- the models are then restructured and retrained based on these observations.
- FIG. 1 shows how the information in the online system flows through the various transformations, model calculations, fuzzy Petri nets and consolidation to arrive at a summary trend which indicates the normality/abnormality of the process areas.
- FIG. 2 shows a valve flow plot to the operator as a simple x-y plot.
- FIG. 3 shows three-dimensional redundancy expressed as a PCA model.
- FIG. 4 shows a schematic diagram of a fuzzy network setup.
- FIG. 5 shows a schematic diagram of the overall process for developing an abnormal event application.
- FIG. 6 shows a schematic diagram of the anatomy of a process control cascade.
- FIG. 7 shows a schematic diagram of the anatomy of a multivariable constraint controller, MVCC.
- FIG. 8 shows a schematic diagram of the on-line inferential estimate of current quality.
- FIG. 9 shows the KPI analysis of historical data.
- FIG. 10 shows a diagram of signal to noise ratio.
- FIG. 11 shows how the process dynamics can disrupt the correlation between the current values of two measurements.
- FIG. 12 shows the probability distribution of process data.
- FIG. 13 shows illustration of the press statistic
- FIG. 14 shows the two-dimensional energy balance model.
- FIG. 15 shows a typical stretch of flow, valve position, and delta pressure data with the long period of constant operation.
- FIG. 16 shows a type 4 fuzzy discriminator.
- FIG. 17 shows a flow versus valve Pareto chart.
- FIG. 18 shows a schematic diagram of operator suppression logic.
- FIG. 19 shows a schematic diagram of event suppression logic.
- FIG. 20 shows the setting of the duration of event suppression.
- FIG. 21 shows the event suppression and the operator suppression disabling predefined sets of inputs in the PCA model.
- FIG. 22 shows how design objectives are expressed in the primary interfaces used by the operator.
- console operator is expected to diagnosis the process problem based on his process knowledge and training.
- the initial decision is to create groups of equipment that will be covered by a single PCA model.
- the specific process units included requires an understanding of the process integration/interaction. Similar to the design of a multivariable constraint controller, the boundary of the PCA model should encompass all significant process interactions and key upstream and downstream indications of process changes and disturbances.
- Equipment groups are defined by including all the major material and energy integrations and quick recycles in the same equipment group (also referred to as key functional sections or operational sections). If the process uses a multivariable constraint controller, the controller model will explicitly identify the interaction points among the process units. Otherwise the interactions need to be identified through an engineering analysis of the process.
- Process groups should be divided at a point where there is a minimal interaction between the process equipment groups. The most obvious dividing point occurs when the only interaction comes through a single pipe containing the feed to the next downstream unit.
- the temperature, pressure, flow, and composition of the feed are the primary influences on the downstream equipment group and the pressure in the immediate downstream unit is the primary influence on the upstream equipment group.
- the process control applications provide additional influence paths between upstream and downstream equipment groups. Both feedforward and feedback paths can exist. Where such paths exist the measurements which drive these paths need to be included in both equipment groups. Analysis of the process control applications will indicate the major interactions among the process units.
- Process operating modes are defined as specific time periods where the process behavior is significantly different. Examples of these are production of different grades of product (e.g. polymer production), significant process transitions (e.g. startups, shutdowns, feedstock switches), processing of dramatically different feedstock (e.g. cracking naphtha rather than ethane in olefins production), or different configurations of the process equipment (different sets of process units running).
- grades of product e.g. polymer production
- significant process transitions e.g. startups, shutdowns, feedstock switches
- processing of dramatically different feedstock e.g. cracking naphtha rather than ethane in olefins production
- different configurations of the process equipment different sets of process units running.
- the signal to noise ratio is a measure of the information content in the input signal.
- the signal to noise ratio is calculated as follows:
- the data set used to calculate the S/N should exclude any long periods of steady-state operation since that will cause the estimate for the noise content to be excessively large.
- the cross correlation is a measure of the information redundancy the input data set.
- the cross correlation between any two signals is calculated as:
- the first circumstance occurs when there is no significant correlation between a particular input and the rest of the input data set. For each input, there must be at least one other input in the data set with a significant correlation coefficient, such as 0.4.
- the second circumstance occurs when the same input information has been (accidentally) included twice, often through some calculation, which has a different identifier. Any input pairs that exhibit correlation coefficients near one (for example above 0.95) need individual examination to determine whether both inputs should be included in the model. If the inputs are physically independent but logically redundant (i.e., two independent thermocouples are independently measuring the same process temperature) then both these inputs should be included in the model.
- the process control system could be configured on an individual measurement basis to either assign a special code to the value for that measurement to indicate that the measurement is a Bad Value, or to maintain the last good value of the measurement. These values will then propagate throughout any calculations performed on the process control system. When the “last good value” option has been configured, this can lead to erroneous calculations that are difficult to detect and exclude. Typically when the “Bad Value” code is propagated through the system, all calculations which depend on the bad measurement will be flagged bad as well.
- Constrained variables are ones where the measurement is at some limit, and this measurement matches an actual process condition (as opposed to where the value has defaulted to the maximum or minimum limit of the transmitter range—covered in the Bad Value section). This process situation can occur for several reasons:
- FIG. 6 shows a typical “cascade” process control application, which is a very common control structure for refining and chemical processes. Although there are many potential model inputs from such an application, the only ones that are candidates for the model are the raw process measurements (the “PVs” in this figure) and the final output to the field valve.
- PVs the raw process measurements
- the PV of the ultimate primary of the cascade control structure is a poor candidate for inclusion in the model.
- This measurement usually has very limited movement since the objective of the control structure is to keep this measurement at the setpoint.
- There can be movement in the PV of the ultimate primary if its setpoint is changed but this usually is infrequent.
- the data patterns from occasional primary setpoint moves will usually not have sufficient power in the training dataset for the model to characterize the data pattern.
- this measurement should be scaled based on those brief time periods during which the operator has changed the setpoint and until the process has moved close to the vale of the new setpoint (for example within 95% of the new setpoint change thus if the setpoint change is from 10 to 11, when the PV reaches 10.95)
- thermocouples located near a temperature measurement used as a PV for an Ultimate Primary. These redundant measurements should be treated in the identical manner that is chosen for the PV of the Ultimate Primary.
- Cascade structures can have setpoint limits on each secondary and can have output limits on the signal to the field control valve. It is important to check the status of these potentially constrained operations to see whether the measurement associated with a setpoint has been operated in a constrained manner or whether the signal to the field valve has been constrained. Date during these constrained operations should not be used.
- FIG. 7 shows a typical MVCC process control application, which is a very common control structure for refining and chemical processes.
- An MVCC uses a dynamic mathematical model to predict how changes in manipulated variables, MVs, (usually valve positions or setpoints of regulatory control loops) will change control variables, CVs (the dependent temperatures, pressures, compositions and flows which measure the process state).
- An MVCC attempts to push the process operation against operating limits. These limits can be either MV limits or CV limits and are determined by an external optimizer. The number of limits that the process operates against will be equal to the number of MVs the controller is allowed to manipulate minus the number of material balances controlled. So if an MVCC has 12 MVs, 30 CVs and 2 levels then the process will be operated against 10 limits.
- An MVCC will also predict the effect of measured load disturbances on the process and compensate for these load disturbances (known as feedforward variables, FF).
- Whether or not a raw MV or CV is a good candidate for inclusion in the PCA model depends on the percentage of time that MV or CV is held against its operating limit by the MVCC. As discussed in the Constrained Variables section, raw variables that are constrained more than 10% of the time are poor candidates for inclusion in the PCA model. Normally unconstrained variables should be handled per the Constrained Variables section discussion.
- an unconstrained MV is a setpoint to a regulatory control loop
- the setpoint should not be included, instead the measurement of that regulatory control loop should be included.
- the signal to the field valve from that regulatory control loop should also be included.
- an unconstrained MV is a signal to a field valve position, then it should be included in the model.
- the process control system databases can have a significant redundancy among the candidate inputs into the PCA model.
- One type of redundancy is “physical redundancy”, where there are multiple sensors (such as thermocouples) located in close physical proximity to each other within the process equipment.
- the other type of redundancy is “calculational redundancy”, where raw sensors are mathematically combined into new variables (e.g. pressure compensated temperatures or mass flows calculated from volumetric flow measurements).
- both the raw measurement and an input which is calculated from that measurement should not be included in the model.
- the general preference is to include the version of the measurement that the process operator is most familiar with.
- the exception to this rule is when the raw inputs must be mathematically transformed in order to improve the correlation structure of the data for the model. In that case the transformed variable should be included in the model but not the raw measurement.
- Physical redundancy is very important for providing cross validation information in the model.
- raw measurements which are physically redundant should be included in the model.
- these measurements must be specially scaled so as to prevent them from overwhelming the selection of principle components (see the section on variable scaling).
- a common process example occurs from the large number of thermocouples that are placed in reactors to catch reactor runaways.
- the developer can identify the redundant measurements by doing a cross-correlation calculation among all of the candidate inputs. Those measurement pairs with a very high cross-correlation (for example above 0.95) should be individually examined to classify each pair as either physically redundant or calculationally redundant.
- Span the normal operating range Datasets, which span small parts of the operating range, are composed mostly of noise. The range of the data compared to the range of the data during steady state operations is a good indication of the quality of the information in the dataset.
- History should be as similar as possible to the data used in the on-line system:
- the online system will be providing spot values at a frequency fast enough to detect the abnormal event. For continuous refining and chemical operations this sampling frequency will be around one minute.
- the training data should be as equivalent to one-minute spot values as possible.
- the strategy for data collection is to start with a long operating history (usually in the range of 9 months to 18 months), then try to remove those time periods with obvious or documented abnormal events. By using such a long time period,
- the training data set needs to have examples of all the normal operating modes, normal operating changes and changes and normal minor disturbances that the process experiences. This is accomplished by using data from over a long period of process operations (e.g. 9-18 months). In particular, the differences among seasonal operations (spring, summer, fall and winter) can be very significant with refinery and chemical processes.
- the model would start with a short initial set of training data (e.g. 6 weeks) then the training dataset is expanded as further data is collected and the model updated monthly until the models are stabilized (e.g. the model coefficients don't change with the addition of new data)
- the various operating journals for this time period should also be collected. This will be used to designate operating time periods as abnormal, or operating in some special mode that needs to be excluded from the training dataset. In particular, important historical abnormal events can be selected from these logs to act as test cases for the models.
- Old data that no longer properly represents the current process operations should be removed from the training data set. After a major process modification, the training data and PCA model may need to be rebuilt from scratch. If a particular type of operation is no longer being done, all data from that operation should be removed from the training data set.
- Operating logs should be used to identify when the process was run under different operating modes. These different modes may require separate models. Where the model is intended to cover several operating modes, the number of samples in the training dataset from each operating model should be approximately equivalent.
- the developer should gather several months of process data using the site's process historian, preferably getting one minute spot values. If this is not available, the highest resolution data, with the least amount of averaging should be used.
- FIG. 8 shows the online calculation of a continuous quality estimate. This same model structure should be created and applied to the historical data. This quality estimate then becomes the input into the PCA model.
- the quality of historical data is difficult to determine.
- the inclusion of abnormal operating data can bias the model.
- the strategy of using large quantities of historical data will compensate to some degree the model bias caused by abnormal operating in the training data set.
- the model built from historical data that predates the start of the project must be regarded with suspicion as to its quality.
- the initial training dataset should be replaced with a dataset, which contains high quality annotations of the process conditions, which occur during the project life.
- the model development strategy is to start with an initial “rough” model (the consequence of a questionable training data set) then use the model to trigger the gathering of a high quality training data set.
- annotations and data will be gathered on normal operations, special operations, and abnormal operations. Anytime the model flags an abnormal operation or an abnormal event is missed by the model, the cause and duration of the event is annotated. In this way feedback on the model's ability to monitor the process operation can be incorporated in the training data.
- This data is then used to improve the model, which is then used to continue to gather better quality training data. This process is repeated until the model is satisfactory.
- the historical data is divided into periods with known abnormal operations and periods with no identified abnormal operations.
- the data with no identified abnormal operations will be the training data set.
- the training data set should now be run through this preliminary model to identify time periods where the data does not match the model. These time periods should be examined to see whether an abnormal event was occurring at the time. If this is judged to be the case, then these time periods should also be flagged as times with known abnormal events occurring. These time periods should be excluded from the training data set and the model rebuilt with the modified data.
- FIG. 9 shows a histogram of a KPI. Since the operating goal for this KPI is to maximize it, the operating periods where this KPI is low are likely abnormal operations. Process qualities are often the easiest KPIs to analyze since the optimum operation is against a specification limit and they are less sensitive to normal feed rate variations.
- Noise we are referring to the high frequency content of the measurement signal which does not contain useful information about the process.
- Noise can be caused by specific process conditions such as two-phase flow across an orifice plate or turbulence in the level. Noise can be caused by electrical inductance. However, significant process variability, perhaps caused by process disturbances is useful information and should not be filtered out.
- the amount of noise in the signal can be quantified by a measure known as the signal to noise ratio (see FIG. 10 ). This is defined as the ratio of the amount of signal variability due to process variation to the amount of signal variability due to high frequency noise. A value below four is a typical value for indicating that the signal has substantial noise, and can harm the model's effectiveness.
- Signals with very poor signal to noise ratios may not be sufficiently improved by filtering techniques to be directly included in the model.
- the scaling of the variable should be set to de-sensitize the model by significantly increasing the size of the scaling factor (typically by a factor in the range of 2-10).
- Transformed variables should be included in the model for two different reasons.
- FIG. 11 shows how the process dynamics can disrupt the correlation between the current values of two measurements.
- one value is constantly changing while the other is not, so there is no correlation between the current values during the transition.
- these two measurements can be brought back into time synchronization by transforming the leading variable using a dynamic transfer function.
- a first order with deadtime dynamic model shown in Equation 9 in the Laplace transform format
- the process measurements are transformed to deviation variables: deviation from a moving average operating point. This transformation to remove the average operating point is required when creating PCA models for abnormal event detection. This is done by subtracting the exponentially filtered value (see Equations 8 and 9 for exponential filter equations) of a measurement from its raw value and using this difference in the model.
- X′ X ⁇ X filtered Equation 10
- the time constant for the exponential filter should be about the same size as the major time constant of the process. Often a time constant of around 40 minutes will be adequate.
- the consequence of this transformation is that the inputs to the PCA model are a measurement of the recent change of the process from the moving average operating point.
- the data In order to accurately perform this transform, the data should be gathered at the sample frequency that matches the on-line system, often every minute or faster. This will result in collecting 525,600 samples for each measurement to cover one year of operating data. Once this transformation has been calculated, the dataset is resampled to get down to a more manageable number of samples, typically in the range of 30,000 to 50,000 samples.
- the model can be built quickly using standard tools.
- the scaling should be based on the degree of variability that occurs during normal process disturbances or during operating point changes not on the degree of variability that occurs during continuous steady state operations.
- First is to identify time periods where the process was not running at steady state, but was also not experiencing a significant abnormal event.
- a limited number of measurements act as the key indicators of steady state operations. These are typically the process key performance indicators and usually include the process feed rate, the product production rates and the product quality. These key measures are used to segment the operations into periods of normal steady state operations, normally disturbed operations, and abnormal operations. The standard deviation from the time periods of normally disturbed operations provides a good scaling factor for most of the measurements.
- the scaling factor can be approximated by looking at the data distribution outside of 3 standard deviations from the mean. For example, 99.7% of the data should lie, within 3 standard deviations of the mean and that 99.99% of the data should lie, within 4 standard deviations of the mean.
- the span of data values between 99.7% and 99.99% from the mean can act as an approximation for the standard deviation of the “disturbed” data in the data set. See FIG. 12 .
- PCA transforms the actual process variables into a set of independent variables called Principal Components, PC, which are linear combinations of the original variables (Equation 13).
- PC i A i,1 *X 1 +A i,2 *X 2 +A i,3 *X 3 + Equation 13
- Process variation can be due to intentional changes, such as feed rate changes, or unintentional disturbances, such as ambient temperature variation.
- Each principal component models a part of the process variability caused by these different independent influences on the process.
- the principal components are extracted in the direction of decreasing variation in the data set, with each subsequent principal component modeling less and less of the process variability.
- Significant principal components represent a significant source of process variation, for example the first principal component usually represents the effect of feed rate changes since this is usually the source of the largest process changes. At some point, the developer must decide when the process variation modeled by the principal components no longer represents an independent source of process variation.
- the engineering approach to selecting the correct number of principal components is to stop when the groups of variables, which are the primary contributors to the principal component no longer make engineering sense.
- the primary cause of the process variation modeled by a PC is identified by looking at the coefficients, A i,n , of the original variables (which are called loadings). Those coefficients, which are relatively large in magnitude, are the major contributors to a particular PC.
- Someone with a good understanding of the process should be able to look at the group of variables, which are the major contributors to a PC and assign a name (e.g. feed rate effect) to that PC.
- the coefficients become more equal in size. At this point the variation being modeled by a particular PC is primarily noise.
- the process data will not have a gaussian or normal distribution. Consequently, the standard statistical method of setting the trigger for detecting an abnormal event at 3 standard deviations of the error residual should not be used. Instead the trigger point needs to be set empirically based on experience with using the model.
- the trigger level should be set so that abnormal events would be signaled at a rate acceptable to the site engineer, typically 5 or 6 times each day. This can be determined by looking at the SPE x statistic for the training data set (this is also referred to as the Q statistic or the DMOD x statistic). This level is set so that real abnormal events will not get missed but false alarms will not overwhelm the site engineer.
- the initial model needs to be enhanced by creating a new training data set. This is done by using the model to monitor the process. Once the model indicates a potential abnormal situation, the engineer should investigate and classify the process situation. The engineer will find three different situations, either some special process operation is occurring, an actual abnormal situation is occurring, or the process is normal and it is a false indication.
- the new training data set is made up of data from special operations and normal operations. The same analyses as were done to create the initial model need to be performed on the data, and the model re-calculated. With this new model the trigger lever will still be set empirically, but now with better annotated data, this trigger point can be tuned so as to only give an indication when a true abnormal event has occurred.
- the “filtered bias” term updates continuously to account for persistent unmeasured process changes that bias the engineering redundancy model.
- the convergence factor, “N”, is set to eliminate any persistent change after a user specified time period, usually on the time scale of days.
- the “normal operating range” and the “normal model deviation” are determined from the historical data for the engineering redundancy model. In most cases the max_error value is a single value, however this can also be a vector of values that is dependent on the x axis location.
- FIG. 14 shows a two dimensional energy balance.
- a particularly valuable engineering redundancy model is the flow versus valve position model. This model is graphically shown in FIG. 2 .
- the particular form of this model is:
- FIG. 15 shows a typical stretch of Flow, Valve Position, and Delta Pressure data with the long periods of constant operation.
- the first step is to isolate the brief time periods where there is some significant variation in the operation, as shown. This should be then mixed with periods of normal operation taken from various periods in history.
- the valve characteristic curve can be either fit with a linear valve curve, with a quadratic valve curve or with a piecewise linear function.
- the piecewise linear function is the most flexible and will fit any form of valve characteristic curve.
- the model is developed in two phases, first where a small dataset, which only contains periods of Delta_Pressure variation is used to fit the model. Then the pressure dependent parameters (“a” and perhaps the missing upstream or downstream pressure) are fixed at the values determined, and the model is re-developed with the larger dataset.
- the “normal operating range” As with any two-dimensional engineering redundancy model, there are two measures of abnormality, the “normal operating range” and the “normal model deviation”.
- the “normal model deviation” is based on a normalized index: the error/max_error. This is fed into a type 4 fuzzy discriminator ( FIG. 16 ). The developer can pick the transition from normal (value of zero) to abnormal (value of 1) in a standard way by using the normalized index.
- the “normal operating range” index is the valve position distance from the normal region. It typically represents the operating region of the valve where a change in valve position will result in little or no change in the flow through the valve.
- a common way of grouping Flow/Valve models which is favored by the operators is to put all of these models into a single fuzzy network so that the trend indicator will tell them that all of their critical flow controllers are working.
- the model indications into the fuzzy network ( FIG. 4 ) will contain the “normal operating range” and the “normal model deviation” indication for each of the flow/valve models.
- the trend will contain the discriminator result from the worst model indication.
- FIG. 17 When a common equipment type is grouped together, another operator favored way to look at this group is through a Pareto chart of the flow/valves ( FIG. 17 ).
- the top 10 abnormal valves are dynamically arranged from the most abnormal on the left to the least abnormal on the right.
- Each Pareto bar also has a reference box indicating the degree of variation of the model abnormality indication that is within normal.
- the chart in FIG. 17 shows that “Valve 10 ” is substantially outside the normal box but that the others are all behaving normally. The operator would next investigate a plot for “Valve 10 ” similar to FIG. 2 to diagnose the problem with the flow control loop.
- This engineering unit version of the model can be converted to a standard PCA model format as follows:
- the multidimensional engineering redundancy model can now be handled using the standard PCA structure for calculation, exception handling, operator display and interaction.
- suppression which is automatically triggered by an external, measurable event
- suppression which is initiated by the operator.
- the logic behind these two types of suppression is shown in FIGS. 18 and 19 . Although these diagrams show the suppression occurring on a fuzzified model index, suppression can occur on a particular measurement, on a particular model index, on an entire model, or on a combination of models within the process area.
- timers For operator initiated suppression, there are two timers, which determine when the suppression is over. One timer verifies that the suppressed information has returned to and remains in the normal state. Typical values for this timer are from 15-30 minutes. The second timer will reactivate the abnormal event check, regardless of whether it has returned to the normal state. Typical values for this timer are either equivalent to the length of the operator's work shift (8 to 12 hours) or a very large time for semi-permanent suppression.
- a measurable trigger is required. This can be an operator setpoint change, a sudden measurement change, or a digital signal. This signal is converted into a timing signal, shown in FIG. 20 .
- timing signal As long as the timing signal is above a threshold (shown as 0.05 in FIG. 20 ), the event remains suppressed.
- the developer sets the length of the suppression by changing the filter time constant, T f . Although a simple timer could also be used for this function, this timing signal will account for trigger signals of different sizes, creating longer suppressions for large changes and shorter suppressions for smaller changes.
- FIG. 21 shows the event suppression and the operator suppression disabling predefined sets of inputs in the PCA model.
- the set of inputs to be automatically suppressed is determined from the on-line model performance. Whenever the PCA model gives an indication that the operator does not want to see, this indication can be traced to a small number of individual contributions to the Sum of Error Square index. To suppress these individual contributions, the calculation of this index is modified as follows:
- the contribution weights are set to zero for each of the inputs that are to be suppressed.
- the contribution weight is gradually returned to a value of 1.
- the model indices can be segregated into groupings that better match the operators' view of the process and can improve the sensitivity of the index to an abnormal event.
- these groupings are based around smaller sub-units of equipment (e.g. reboiler section of a tower), or are sub-groupings, which are relevant to the function of the equipment (e.g. product quality).
- each principle component can be subdivided to match the equipment groupings and an index analogous to the Hotelling T 2 index can be created for each subgroup.
- the thresholds for these indices are calculated by running the testing data through the models and setting the sensitivity of the thresholds based on their performance on the test data.
- Inputs will appear in several PCA models so that all interactions affecting the model are encompassed within the model. This can cause multiple indications to the operator when these inputs are the major contributors to the sum of error squared index.
- any input which appears in multiple PCA models, is assigned one of those PCA models as its primary model.
- the contribution weight in Equation 29 for the primary PCA model will remain at one while for the non-primary PCA models, it is set to zero.
- the primary objectives of the operator interface are to:
- the final output from a fuzzy Petri net is a normality trend as is shown in FIG. 4 .
- This trend represents the model index that indicates the greatest likelihood of abnormality as defined in the fuzzy discriminate function.
- the number of trends shown in the summary is flexible and decided in discussions with the operators.
- On this trend are two reference lines for the operator to help signal when they should take action, a line typically set at a value of 0.6 and a line typically set at a value of 0.9. These lines provide guidance to the operator as to when he is expected to take action.
- the triangle in FIG. 4 will turn yellow and when the trend crosses the 0.9 line, the triangle will turn red.
- the triangle also has the function that it will take the operator to the display associated with the model giving the most abnormal indication.
- the model is a PCA model or it is part of an equipment group (e.g. all control valves)
- selecting the triangle will create a Pareto chart.
- a PCA model of the dozen largest contributors to the model index, this will indicate the most abnormal (on the left) to the least abnormal (on the right).
- the key abnormal event indicators will be among the first 2 or 3 measurements.
- the Pareto chart includes a box around each bar to provide the operator with a reference as to how unusual the measurement can be before it is regarded as an indication of abnormality.
Abstract
Description
-
- criteria for establishing the equipment scope of the PCA models criteria and methods for selecting, analyzing, and transforming measurement inputs
- developing of multivariate statistical models based on a variation of principle component models, PCA
- developing models based on simple engineering relationships restructuring the associated statistical indices
- preprocessing the on-line data to provide exception calculations and continuous on-line model updating
- using fuzzy Petri nets to interpret model indices as normal or abnormal
- using fuzzy Petri nets to combine multiple model outputs into a single continuous summary indication of normality/abnormality for a process area
- design of operator interactions with the models and fuzzy Petri nets to reflect operations and maintenance activities
-
- 1. The process is stationary—its mean and variance are constant over time.
- 2. The cross correlation among variables is linear over the range of normal process operations
- 3. Process noise random variables are mutually independent.
- 4. The covariance matrix of the process variables is not degenerate (i.e. positive semi-definite).
- 5. The data are scaled “appropriately” (the standard statistical approach being to scale to unit variance).
- 6. There are no (uncompensated) process dynamics (a standard partial compensation for this being the inclusion of lag variables in the model)
- 7. All variables have some degree of cross correlation.
- 8. The data have a multivariate normal distribution
-
-
temperature 1=temperature 2 -
flow 1=valve characteristic curve 1 (valve 1 position) - material flow into
process unit 1=material flow out ofprocess unit 1
-
-
-
pressure 1=pressure 2= . . . =pressure n - material flow into
process unit 1=material flow out ofprocess unit 1= . . . =material flow intoprocess unit 2
-
-
- opening of bypass valves around flow meters
- compensating for upstream/downstream pressure changes
- recalibration of field measurements
- redirecting process flows based on operating modes
-
- provide the console operator with a continuous status (normal vs. abnormal) of process operations for all of the process units under his operating authority
- provide him with an early detection of a rapidly developing (minutes to hours) abnormal event within his operating authority
- provide him with only the key process information needed to diagnose the root cause of the abnormal event.
-
- Subdividing the process equipment into equipment groups with corresponding PCA models
- Subdividing process operating time periods into process operating modes requiring different PCA models
- Identifying which measurements within an equipment group should be designated as inputs to each PCA model
- Identifying which measurements within an equipment group should act as flags for suppressing known events or other exception operations
A. Process Unit Coverage
- 1. Can the problem be permanently fixed? Often a problem exists because site personnel have not had sufficient time to investigate and permanently solve the problem. Once the attention of the organization is focused on the problem, a permanent solution is often found. This is the best approach.
- 2. Can the problem be directly measured? A more reliable way to detect a problem is to install sensors that can directly measure the problem in the process. This can also be used to prevent the problem through a process control application. This is the second best approach.
- 3. Can an inferential measurement be developed which will measure the approach to the abnormal operation? Inferential measurements are usually developed using partial least squares, PLS, models which are very close relatives to PCA abnormal event models. Other common alternatives for developing inferential measurements include Neural Nets and linear regression models. If the data exists which can be used to reliably measure the approach to the problem condition (e.g. tower flooding using delta pressure), this can then be used to not only detect when the condition exists but also as the base for a control application to prevent the condition from occurring. This is the third best approach.
-
- Select all cascade secondary controller measurements, and especially ultimate secondary outputs (signals to field control valves) on these units
- Select key measurements used by the console operator to monitor the process (e.g. those which appear on his operating schematics)
- Select any measurements used by the contact engineer to measure the performance of the process
- Select any upstream measurement of feedrate, feed temperature or feed quality
- Select measurements of downstream conditions which affect the process operating area, particularly pressures.
- Select extra redundant measurements for measurements that are important
- Select measurements that may be needed to calculate non-linear transformations.
- Select any external measurement of a disturbance (e.g. ambient temperature)
- Select any other measurements, which the process experts regard as important measures of the process condition
-
- The measurement does not have a history of erratic or problem performance
- The measurement has a satisfactory signal to noise ratio
- The measurement is cross-correlated with other measurements in the data set
- The measurement is not saturated for more than 10% of the time during normal operations.
- The measurement is not tightly controlled to a fixed setpoint, which rarely changes (the ultimate primary of a control hierarchy).
- The measurement does not have long stretches of “Bad Value” operation or saturated against transmitter limits.
- The measurement does not go across a range of values, which is known to be highly non-linear
- The measurement is not a redundant calculation from the raw measurements
- The signals to field control valves are not saturated for more than 10% of the time
A. Evaluations for Selecting Model Inputs
- 1. The raw signal is filtered using an exponential filter with an approximate dynamic time constant equivalent to that of the process. For continuous refining and chemical processes this time constant is usually in the range of 30 minutes to 2 hours. Other low pass filters can be used as well. For the exponential filter the equations are:
Y n =P*Y n−1+(1−P)*X n Exponentialfilter equation Equation 1
P=Exp(−T s /T f) Filterconstant calculation Equation 2- where:
- Yn the current filtered value
- Yn−1 the previous filtered value
- Xn the current raw value
- P the exponential filter constant
- Ts the sample time of the measurement
- Tf the filter time constant
- where:
- 2. A residual signal is created by subtracting the filtered signal from the raw signal
R n =X n −Y n Equation 3 - 3. The signal to noise ratio is the ratio of the standard deviation of the filtered signal divided by the standard deviation of the residual signal
S/N=σ Y/σR Equation 4
- 1. Calculate the co-variance, Sik, between each input pair, i and k
- 2. Calculate the correlation coefficient for each pair of inputs from the co-variance:
CC ik =S ik/(S ii *S kk)1/2Equation 6
-
- The actual process condition is outside the range of the field transmitter
- The connection with the field has been broken
-
- Portions of the process are normally inactive except under special override conditions, for example pressure relief flow to the flare system. Time periods where these override conditions are active should be excluded from the training and validation data set by setting up data filters. For the on-line implementation these override events are trigger events for automatic suppression of selected model statistics
- The process control system is designed to drive the process against process operating limits, for example product spec limits. These constraints typically fall into two categories:—those, which are occasionally saturated and those, which are normally saturated. Those inputs, which are normally saturated, should be excluded from the model. Those inputs, which are only occasionally saturated (for example less than 10% of the time) can be included in the model however, they should be scaled based on the time periods when they are not saturated.
B. Input from Process Control Applications
-
- The variation of controlled variables is significantly reduced so that movement in the controlled variables is primarily noise except for those brief time periods when the process has been hit with a significant process disturbance or the operator has intentionally moved the operating point by changing key setpoints.
- The normal variation in the controlled variables is transferred by the control system to the manipulated variables (ultimately the signals sent to the control valves in the field).
-
- the smaller abnormal events will not appear with sufficient strength in the training data set to significantly influence the model parameters
- most operating modes should have occurred and will be represented in the data.
A. Historical Data Collection Issues
1) Data Compression
-
- Those with many long periods of time as “Bad Value”
- Those with many long periods of time pegged to their transmitter high or low limits
- Those, which show very little variability (except those, which are tightly controlled to their setpoints)
- Those that continuously show very large variability relative to their operating range
- Those that show little or no cross correlation with any other measurements in the data set
- Those with poor signal to noise ratios
X′=(X−X avg)/σ Equation 7
-
- Fix the signal by removing the source of the noise (the best answer)
- Remove/minimize the noise through filtering techniques
- Exclude the signal from the model
Y n =P*Y n−1+(1−P)*X n Exponential
P=Exp(−T s /T f) Filter
-
- Yn is the current filtered value
- Yn−1 is the previous filtered value
- Xn is the current raw value
- P is the exponential filter constant
- Ts is the sample time of the measurement
- Tf is the filter time constant
-
- Reflux to feed ratio in distillation columns
- Log of composition in high purity distillation
- Pressure compensated temperature measurement
- Sidestream yield
- Flow to valve position (
FIG. 2 ) - Reaction rate to exponential temperature change
-
- Y—raw data
- Y′—time synchronized data
- T—time constant
- Θ—deadtime
- S—Laplace Transform parameter
X′=X−X filtered Equation 10
-
- X′—measurement transformed to remove operating point changes
- X—original raw measurement
- Xfiltered—exponentially filtered raw measurement
X i ′=X i/σi Equation 11
X i ′=X i/((σi*sqrt(N))
-
- Where N=number of inputs in redundant data group
PC i =A i,1 *X 1 +A i,2 *X 2 +A i,3 *X 3+
F(y i)=G(x i)+filtered biasi+operator bias+errori Equation 14
raw biasi =F(y i)−{G(x i)+filtered biasi+operator bias}=erroriEquation 15
filtered biasi=filtered biasi−1 +N*raw biasi−1 Equation 16
-
- N—convergence factor ( e.g. 0.0001 )
- Normal operating range: xmin<x<xmax
- Normal model deviation: −(max_error)<error<(max_error)
where:
-
- Flow: measured flow through a control valve
- Delta_Pressure=closest measured upstream pressure−closest measured downstream pressure
- Delta_Pressurereference: average Delta_Pressure during normal operation
- a: model parameter fitted to historical data
- Cv: valve characteristic curve determined empirically from historical data
- VP: signal to the control valve (not the actual control valve position)
The objectives of this model are to: - Detecting sticking/stuck control valves
- Detecting frozen/failed flow measurements
- Detecting control valve operation where the control system loses control of the flow
-
-
pressure 1=pressure 2= . . . =pressure n - material flow into
process unit 1=material flow out ofprocess unit 1= . . . =material flow intoprocess unit 2
-
F 1(y i)=a 1 G 1 (x i)+filtered bias1,1+operator biasi+error1,i
F 2(y i)=a n G 2 (x i)+filtered bias2,i+operator bias2+error2,i
F n(y i)=a n G n (x i)+filtered biasn,i+operator biasn+errorn,i Equation 18
P=a 1 X 1+a 2 X 2+a 3 X 3 Equation 19
-
- Where a3=1
X scale =X normal operating range/
-
- (99.7% of normal operating data should fall within 3 σ of the mean)
Xmid=Xmid point of operating range Equation 21 - (explicitly defining the “mean” as the mid point of the normal operating range)
X′=(X−X mid)/X scale Equation 22 - (standard PCA scaling once mean and C are determined)
Then the P′ Loadings for Xi are:
- (99.7% of normal operating data should fall within 3 σ of the mean)
-
- (the requirement that the loading vector be normalized)
This Transforms P to
P′=b 1 *X 1+b 2 *X 2+ . . . +b n *XN Equation 24
P′ “standard deviation”=b 1 +b 2 + . . . +b n Equation 25
- (the requirement that the loading vector be normalized)
-
- Provide a way to eliminate false indications from measurable unusual events
- Provide a way to clear abnormal indications that the operator has investigated
- Provide a way to temporarily disable models or measurements for maintenance
- Provide a way to disable bad acting models until they can be retuned
- Provide a way to permanently disable bad acting instruments.
Y n =P*Y n−1+(1−P)*X n Exponential filter equation Equation 26
P=Exp(−T s /T f) Filter constant calculation Equation 27
Z n =X n −Y n Timing
-
- where:
- Yn the current filtered value of the trigger signal
- Yn−1 the previous filtered value of the trigger signal
- Xn the current value of the trigger signal
- Zn the timing signal shown in
FIG. 20 - P the exponential filter constant
- Ts the sample time of the measurement
- Tf the filter time constant
- where:
-
- wi—the contribution weight for input i (normally equal to 1)
- ei—the contribution to the sum of error squared from input i
-
- Provide a continuous indication of the normality of the major process areas under the authority of the operator
- Provide rapid (1 or 2 mouse clicks) navigation to the underlying model information
- Provide the operator with control over which models are enabled.
FIG. 22 shows how these design objectives are expressed in the primary interfaces used by the operator.
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Also Published As
Publication number | Publication date |
---|---|
WO2006031635A3 (en) | 2006-12-14 |
NO20071830L (en) | 2007-05-09 |
CA2578612A1 (en) | 2006-03-23 |
JP2008512792A (en) | 2008-04-24 |
JP5364265B2 (en) | 2013-12-11 |
US20060058898A1 (en) | 2006-03-16 |
NO338660B1 (en) | 2016-09-26 |
EP1789856A4 (en) | 2008-10-29 |
WO2006031635A2 (en) | 2006-03-23 |
CA2578612C (en) | 2014-08-26 |
EP1789856A2 (en) | 2007-05-30 |
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